With rapid integration of motor vehicle with wireless network, AI, and IoT (Internet of Things), the demand of intelligent machine and instant response is constantly growing. For example, the cars or vehicles which become smarter can assist drivers to operate the vehicles. To implement the integration of vehicle and AI, some technical pieces, such as data management, model training, and data collection, need to be improved. The conventional machine learning process, for example, is generally an exploratory process which may involve trying different kinds of models, such as convolutional, RNN (recurrent neural network), attentional, et cetera.
Machine learning or model training typically concerns a wide variety of hyper-parameters that change the shape of the model and training characteristics. Model training generally requires intensive computation and data collection. With conventional data collection via IoT, AI, real-time images, videos, and/or machine learning, the size of data (real-time data, cloud data, big data, etc.) is voluminous and becomes difficult to handle and digest. As such, real-time response via machine learning model with massive data processing can be challenging. Another drawback associated with large data processing and machine learning for model improvements is that it is often difficult to translate collected data into useful information.